Generating Multiple Imputations for Matrix Sampling Data Analyzed with Item Response Models.
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| Title: | Generating Multiple Imputations for Matrix Sampling Data Analyzed with Item Response Models. |
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| Language: | English |
| Authors: | Thomas, Neal, Gan, Nianci |
| Source: | Journal of Educational and Behavioral Statistics. Win 1997 22(4):425-445. |
| Peer Reviewed: | Y |
| Page Count: | 21 |
| Publication Date: | 1997 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Data Analysis, Item Response Theory, Matrices, Maximum Likelihood Statistics, Models, Research Design, Sampling |
| Assessment and Survey Identifiers: | National Assessment of Educational Progress |
| ISSN: | 1076-9986 |
| Abstract: | Describes and assesses missing data methods currently used to analyze data from matrix sampling designs implemented by the National Assessment of Educational Progress. Several improved methods are developed, and these models are evaluated using an EM algorithm to obtain maximum likelihood estimates followed by multiple imputation of complete data sets. (SLD) |
| Entry Date: | 1998 |
| Accession Number: | EJ564705 |
| Database: | ERIC |
| Abstract: | Describes and assesses missing data methods currently used to analyze data from matrix sampling designs implemented by the National Assessment of Educational Progress. Several improved methods are developed, and these models are evaluated using an EM algorithm to obtain maximum likelihood estimates followed by multiple imputation of complete data sets. (SLD) |
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| ISSN: | 1076-9986 |
